Volition
Agentic frameworks that turn human intention into structured deliverables through linguistic optimization
The Problem
Complex projects require repetitive prompt engineering and lack reusable, version-controlled agent templates that can compound and evolve
The Story Behind This App
Mission
Full project lifecycle agentic frameworks and prompt libraries that utilize volitional action of agents to provide structured outputs via deliverables or downstream field generation via sub-agents. Creates reusable templates to accomplish complex tasks that may compound and complete other complex tasks down the road.
The Volitional Deliverable F(x) Machine
The building block of the human-monitored Agent:
- User provides .yaml (user configured)
- YAML configures XML (prompt library provided)
- Functions have detailed output specifications
- Runtime execution via deliverables export
- Deployment to user environments (manual/automatic)
Core Manifesto
- Version Control First: Mutable, repeatable prompts from scratch with dynamic fields
- Function-Like Interaction: Inputs beget outputs in a predictable, machine-like manner
- Generalized Templates: Larger generalized templates over specialized ones
- Work Breakdown Structure: Complex outputs reduced to simple, manageable deliverables
- Omni-Channel Vision: Agents push to multiple environments seamlessly
- Multi-Modal Approach: Vision, auditory, speech, visuals, touch integration
- Tested Libraries: Version controlled prompt libraries over iterative MVPs
- One-Click Orchestration: Streamlined execution over continual prompting
Linguistic Framework for Agent Design
The Human-as-Machine Model
Understanding data structures in LLMs through linguistic structures, optimized for human language and communication patterns backed by neuroscience and biomimicry.
1. Linguistic Components
- Nouns (Entities/Objects): Deliverables, knowledge bases, scopes, WBS components
- Verbs (Actions/Processes): Analyze, execute, refactor, optimize, deploy
- Adjectives (Properties/Verticals): Technical, architectural, strategic
- Adverbs (Modifiers): Iteratively, incrementally, collaboratively
- Prepositions (Relationships): Within scope, across teams, through processes
- Interrogatives (Questions): How, what, who, when, where, why
2. Brain-Computer Architecture Parallels
Volitional and Reflexive Synapses
Core Mappings:
- Working memory β RAM (volatile, immediate access)
- Long-term memory β Disk writes (persistent storage)
- Visual processing β Graphics pipeline (hierarchical extraction)
- Motor cortex β Action/Tool execution layer
Key Insight: Motor preparation as cache - the brain pre-computes movement patterns before execution, mapping directly to:
- Function call preparation in LLMs
- Tool-use priming through context
- Pre-computed responses for common patterns
3. Project Architecture
Projects as organized memory and action structures:
- WBS as Brain Repository: The things your brain composes to create volitional action
- Configured Templates: Runtime-ready plans called βVolitionsβ
- Cascading Runbooks: Volitions nesting into larger workflows
- Dynamic Fields: Self-configuring parameters for future runtimes
Technical Implementation
Configuration Pipeline
# User-provided YAML
volition:
name: "Project Scaffold"
type: "development"
outputs:
- deliverable: "architecture_doc"
- deliverable: "test_suite"
- deliverable: "deployment_config"
XML Prompt Library
Detailed function specifications with:
- Input parameters
- Processing logic
- Output schemas
- Error handling
- Multi-modal interfaces
Deliverable Export
- Structured outputs to multiple environments
- Version-controlled artifacts
- Automated or manual deployment
- Cross-platform compatibility
Use Cases
- Software Development: Complete project scaffolding with architecture, tests, and deployment
- Content Creation: Multi-format content generation with consistent voice
- Research Projects: Structured research outputs with citations and analysis
- Business Process: Automated workflow generation and optimization
- Educational Content: Curriculum development with assessments and materials
Integration Points
- Version Control Systems: Git, SVN for prompt library management
- CI/CD Pipelines: Automated deliverable deployment
- Project Management Tools: JIRA, Asana integration for WBS
- Documentation Systems: Automated doc generation
- Multi-Modal Platforms: Voice, visual, and text interfaces
Future Vision
Volition aims to bridge the gap between human intention and machine execution, creating a seamless flow from thought to deliverable through linguistically-optimized, neuroscience-backed agent frameworks that understand and replicate human cognitive patterns in project execution.
Key Features
1. Volitional Deliverable F(x) Machine
What: YAML-configured XML runtime that transforms inputs into structured deliverables
Why: Creates predictable, machine-like outputs from human intentions
2. Linguistic Framework Engine
What: Maps human language patterns (nouns, verbs, adjectives) to agent actions
Why: Optimizes agent understanding through neuroscience-backed linguistic structures
3. Cascading Volitions
What: Nested agent workflows with dynamic field propagation
Why: Enables complex tasks to compound into larger automated processes
4. Multi-Modal Agent Interface
What: Supports vision, auditory, speech, visual, and touch modalities
Why: Allows agents to work across diverse input/output channels
5. Version-Controlled Prompt Libraries
What: Git-managed, tested prompt templates with semantic versioning
Why: Ensures repeatability and evolution of agent capabilities over time
User Journey
- 1 User defines project requirements in YAML configuration
- 2 System generates XML prompt from library templates
- 3 Agent executes volitional actions based on linguistic patterns
- 4 Deliverables are generated with detailed specifications
- 5 Outputs deploy to user environments (manual or automatic)
- 6 Results cascade to trigger downstream volitions if configured
Technical Architecture
Frontend
YAML configuration interface with visual workflow builder
Backend
Node.js runtime with XML parser and agent orchestration
Data
PostgreSQL for prompt libraries, Redis for runtime state
APIs
- OpenAI API
- Anthropic Claude
- GitHub Actions
- CI/CD webhooks
Hosting
Docker containers with Kubernetes orchestration
Moonshot Features (v2.0)
- β Brain-computer parallel processing with motor preparation caching
- β Self-evolving prompt libraries through usage analysis
- β Cross-project knowledge transfer between volitions
- β Real-time agent collaboration with conflict resolution
Market Research
Similar to: LangChain, AutoGPT, CrewAI, Microsoft AutoGen
Different because: Focuses on linguistic optimization and neuroscience-backed patterns rather than pure technical orchestration
Target users: Technical teams, project managers, and organizations seeking repeatable AI workflows
Open Questions
- How to balance generalized vs specialized templates?
- What's the optimal granularity for volition composition?
- How to handle versioning conflicts in cascading workflows?
- Can we achieve true one-click orchestration for enterprise workflows?
Resources & Inspiration
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